Project Summary The morphology (or shape) of anatomical structures forms the common language among clinicians, where ab- normalities in anatomical shapes are often tied to deleterious function. While these observations are often quali- tative, ?nding subtle, quantitative shape effects requires the application of mathematics, statistics, and computing to parse the anatomy into a numerical representation that will facilitate testing of biologically relevant hypotheses. Particle-based shape modeling (PSM) and its associated suite of software tools, ShapeWorks, enable learning population-level shape representation via automatic dense placement of homologous landmarks on image seg- mentations of general anatomy with arbitrary topology. The utility of ShapeWorks has been demonstrated in a range of biomedical applications. Despite its obvious utility for the research enterprise and highly permissive open-source license, ShapeWorks does not have a viable commercialization path due to the inherent trade-off between development and maintenance costs, and a specialized scienti?c and clinical market. ShapeWorks has the potential to transform the way researchers approach studies of anatomical forms, but its widespread ap- plicability to medicine and biology is hindered by several barriers that most existing shape modeling packages face. The most important roadblocks are (1) the complexity and steep learning curve of existing shape modeling pipelines and their increased computational and computer memory requirements; (2) the considerable expertise, time, and effort required to segment anatomies of interest for statistical analyses; and (3) the lack of interoperable implementations that can be readily incorporated into biomedical research laboratories. In this project, we pro- pose ShapeWorksStudio, a software suite that leverages ShapeWorks for the automated population-/patient-level modeling of anatomical shapes, and Seg3D ? a widely used open-source tool to visualize and process volumet- ric images ? for ?exible manual/semiautomatic segmentation and interactive manual correction of segmented anatomy. In Aim 1, we will integrate ShapeWorks and Seg3D in a framework that supports big data cohorts to enable users to transparently proceed from image data to shape models in a straightforward manner. In Aim 2, we will endow Seg3D with a machine learning approach that provides automated segmentations within a statisti- cal framework that combines image data with population-speci?c shape priors provided by ShapeWorks. In Aim 3, we will support interoperability with existing open-source software packages and toolkits, and provide bindings to commonly used programming languages in the biomedical research community. To promote reproducibility, we will develop and disseminate standard work?ows and domain-speci?c test cases. This project combines an interdisciplinary research and development team with decades of experience in statistical analysis and image understanding, and application scientists to con?rm that the proposed developments have a real impact on the biomedical and clinical research communities. Our long-term goal is to make ShapeWorks a standard tool for shape analyses in medicine, and the work proposed herein will establish the groundwork for achieving this goal.